Harnessing AI for Enhanced Student Learning and Performance in Higher Education: A Multinational Collaboration

Abstract

Purpose: Integrating technology into instruction in the ever-emergent landscape of tertiary education has become germane for ensuring quality and relevance in both research and instruction. Particularly in higher education within the field of social sciences and humanities, there exists a pressing need for inventive learning methods that can precisely deliver student learning outcomes. Besides facilitate personalized learning experiences and optimize instructional strategies. Artificial Intelligence (AI) presents a promising opening for addressing these challenges by offering sophisticated analytical tools capable of providing actionable acumen and processing huge volumes of data. The purpose of this research is to assess the impact of AI on students' learning behaviours in tertiary education. Additionally, the study examines moral responsibility in use, ethical obligations, effectiveness, and scalability in AI adoption across three nations Malaysia, India, and Iran. Design/methodology/approach – This research uses qualitative methodology, thematic analysis which is regarded as the most evident approach that delivers a single interpretation. Respondents for this study were postgraduate students, and purposive sampling was employed to select the participants. A total of 29 DBA and MBA students were selected to contribute to this study. Data was analyzed using NVivo software. Findings – The findings for this study shows that AI-powered student learning pathways are influenced by the following factors: (i) students’ anticipation of learning, (ii) execution, the actual learning process (i.e., (iii) symbiotic metamorphosis), cognitive abilities, technological skills, and educational tools (i.e., (iv) socio-technical capital). Furthermore, moral principles, such as utilitarianism and ethical considerations, serve as mediating factors that ensure transparency and freedom from bias. These key variables are essential for enhancing student learning and performance. These findings support foundational AI theory, which was conducted from an employee perspective. Practical implications – The body of knowledge from this research contributes to SDG 4, recognized by the United Nations (2015) as part of the 2030 agenda for Sustainable Development. The findings align with the goal of providing quality education across nations to address global challenges, particularly inequality.